The Experts below are selected from a list of 151917 Experts worldwide ranked by ideXlab platform
Kazuhiko Yokosawa - One of the best experts on this subject based on the ideXlab platform.
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To see dynamic change: continuous focused attention facilitates change Detection, but the effect persists briefly
Visual Cognition, 2017Co-Authors: Ryoichi Nakashima, Kazuhiko YokosawaAbstract:What is the role of continuously focused attention on an object in change Detection? To ensure focused attention on one object, we conducted a single object change Detection Task, manipulat...
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The properties of visual short-term memory on change Detection Task
Japanese Journal of Psychology, 2003Co-Authors: Makoto Ozeki, Kazuhiko YokosawaAbstract:Visual short-term memory (VSTM) has been investigated with a change Detection Task. Recent studies suggested that there might be some representations in VSTM even when a change was not detected. However, this is discrepant with the previous studies that estimated the representation by change Detection. In this study, we investigated the properties of the representation to be retained between two stimuli in a change Detection Task combined with the probe method so as to explore what causes the discrepancy. The interval between the test and the comparison stimuli and the timing of a positional cue at the location of change were manipulated. The results of three experiments suggested that, before the comparison stimulus presentation, the representations in VSTM were retained more than representations estimated by a normal change Detection Task, that they decayed with time, and that their availability decreased when the representations of the comparison stimulus were formed. From these results, we discussed a model of VSTM with attention.
Shen-shyang Ho - One of the best experts on this subject based on the ideXlab platform.
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exploiting sparsity for image based object surface anomaly Detection
International Conference on Acoustics Speech and Signal Processing, 2016Co-Authors: Woon Huei Chai, Shen-shyang HoAbstract:The anomaly Detection Task plays an important role in quality control in many industrial or manufacturing processes. However, in many such processes, anomaly Detection is done visually by human experts who have in-depth knowledge and vast experience on a product in order to perform well in the Detection Task. In this paper, we present an approach that (i) identifies anomalies in an image based on the sparse residuals (or errors) obtained during image reconstruction using sparse representation and (ii) learns the threshold to classify an image pixel based on its residual value. The intuitions for our proposed sparse approximation driven approach are, namely: (i) anomalies are infrequent and (ii) anomalies are unwanted portions of an image reconstruction. Empirical results on a real-world image dataset for an industrial surface defect Detection Task are used to demonstrate the feasibility of our proposed approach.
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ICASSP - Exploiting sparsity for image-based object surface anomaly Detection
2016 IEEE International Conference on Acoustics Speech and Signal Processing (ICASSP), 2016Co-Authors: Woon Huei Chai, Shen-shyang HoAbstract:The anomaly Detection Task plays an important role in quality control in many industrial or manufacturing processes. However, in many such processes, anomaly Detection is done visually by human experts who have in-depth knowledge and vast experience on a product in order to perform well in the Detection Task. In this paper, we present an approach that (i) identifies anomalies in an image based on the sparse residuals (or errors) obtained during image reconstruction using sparse representation and (ii) learns the threshold to classify an image pixel based on its residual value. The intuitions for our proposed sparse approximation driven approach are, namely: (i) anomalies are infrequent and (ii) anomalies are unwanted portions of an image reconstruction. Empirical results on a real-world image dataset for an industrial surface defect Detection Task are used to demonstrate the feasibility of our proposed approach.
John F Culling - One of the best experts on this subject based on the ideXlab platform.
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Measurement of the binaural temporal window using a lateralisation Task.
Hearing Research, 2008Co-Authors: Andrew J. Kolarik, John F CullingAbstract:Binaural temporal resolution was measured using the discrimination of brief interaural time delays (ITDs). In experiment 1, three listeners performed a 2I-2AFC, ITD-discrimination procedure. ITD changes of 8 to 1024 μs were applied to brief probe noises. These probes, with durations of 16 to 362 ms, were placed symmetrically in time within a 500-ms burst of otherwise interaurally uncorrelated noise. Psychometric functions were measured to obtain thresholds and temporal windows fitted to those thresholds. The best-fitting window was a symmetric roex shape (equivalent rectangular duration = 197 ms), an order of magnitude longer than monaural temporal windows and differed substantially from windows reported by Bernstein et al. [Bernstein, L.R., Trahiotis, C., Akeroyd, M.A., Hartung, K., 2001. Sensitivity to brief changes of interaural time and interaural intensity. J. Acoust. Soc. Am. 109, 1604–1615]. Experiment 2, replicated their main experiment, comparing their ITD-Detection Task with a similar discrimination procedure. Thresholds in the Detection conditions were significantly better than those in the discrimination condition, particularly for short probe durations, indicating the use of an additional cue at these durations for the Detection Task and thus undermining the assumptions made in their window fit.
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measurements of the binaural temporal window using a Detection Task
Journal of the Acoustical Society of America, 1998Co-Authors: John F Culling, Quentin SummerfieldAbstract:Two experiments investigated the shape of the binaural temporal window using a Detection Task. In experiment 1, a 10-ms tone burst was presented binaurally out-of-phase during a burst of white noise, which changed from being interaurally uncorrelated, to correlated, and back to uncorrelated. The tone occurred during the correlated portion of the noise in one interval of each 2I-FC trial. Detection thresholds were recorded using a 2-down/1-up adaptive procedure. Thresholds were measured for different durations of correlated noise (0–960 ms), frequencies of tone burst (125, 250, 500, and 1000 Hz) and levels of noise [20, 30, 40, and 50 dB(SPL)/Hz]. Window shapes based on nine candidate functions were fitted to the data using the assumption that the binaural masking release was related to the overall interaural correlation of noise admitted by the window. Fitted windows included both a forward and a backward lobe. Gaussian functions tended to give closer fits than exponential and rounded-exponential functions, and simple functions gave more parsimonious fits that those which included dynamic-range-limiting terms. Using simple Gaussian fits, the shape of the window was largely independent of frequency and level, and the windows for individual listeners had equivalent rectangular durations ranging from 55 to 188 ms. The asymmetry was variable, although forward lobes were generally shorter than backward lobes. Experiment 2 ruled out the possibility that the forward lobe might be an artefact caused by distraction of the listener, when the interaural phase change in the noise closely followed the signal. In this experiment, the out-of-phase tone was presented during a burst of partially correlated noise which changed, after a variable interval, to a fully correlated noise. Thresholds for detecting the tone rose (i.e., performance worsened) as the interval was increased. Distraction would have produced the opposite effect.
Steven J. Luck - One of the best experts on this subject based on the ideXlab platform.
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An eye tracking investigation of color-location binding in infants' visual short-term memory.
Infancy, 2017Co-Authors: Lisa M. Oakes, Heidi A. Baumgartner, Shipra Kanjlia, Steven J. LuckAbstract:Two experiments examined 8- and 10-month-old infants' (N = 71) binding of object identity (color) and location information in visual short-term memory (VSTM) using a one-shot change Detection Task. Building on previous work using the simultaneous streams change Detection Task, we confirmed that 8- and 10-month-old infants are sensitive to changes in binding between identity and location in VSTM. Further, we demonstrated that infants recognize specifically what changed in these events. Thus, infants' VSTM for binding is robust and can be observed in different procedures and with different stimuli.
Woon Huei Chai - One of the best experts on this subject based on the ideXlab platform.
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exploiting sparsity for image based object surface anomaly Detection
International Conference on Acoustics Speech and Signal Processing, 2016Co-Authors: Woon Huei Chai, Shen-shyang HoAbstract:The anomaly Detection Task plays an important role in quality control in many industrial or manufacturing processes. However, in many such processes, anomaly Detection is done visually by human experts who have in-depth knowledge and vast experience on a product in order to perform well in the Detection Task. In this paper, we present an approach that (i) identifies anomalies in an image based on the sparse residuals (or errors) obtained during image reconstruction using sparse representation and (ii) learns the threshold to classify an image pixel based on its residual value. The intuitions for our proposed sparse approximation driven approach are, namely: (i) anomalies are infrequent and (ii) anomalies are unwanted portions of an image reconstruction. Empirical results on a real-world image dataset for an industrial surface defect Detection Task are used to demonstrate the feasibility of our proposed approach.
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ICASSP - Exploiting sparsity for image-based object surface anomaly Detection
2016 IEEE International Conference on Acoustics Speech and Signal Processing (ICASSP), 2016Co-Authors: Woon Huei Chai, Shen-shyang HoAbstract:The anomaly Detection Task plays an important role in quality control in many industrial or manufacturing processes. However, in many such processes, anomaly Detection is done visually by human experts who have in-depth knowledge and vast experience on a product in order to perform well in the Detection Task. In this paper, we present an approach that (i) identifies anomalies in an image based on the sparse residuals (or errors) obtained during image reconstruction using sparse representation and (ii) learns the threshold to classify an image pixel based on its residual value. The intuitions for our proposed sparse approximation driven approach are, namely: (i) anomalies are infrequent and (ii) anomalies are unwanted portions of an image reconstruction. Empirical results on a real-world image dataset for an industrial surface defect Detection Task are used to demonstrate the feasibility of our proposed approach.
